Skip to content

README.md

Latest commit

 

History

History
54 lines (40 loc) · 2.04 KB

README.md

File metadata and controls

54 lines (40 loc) · 2.04 KB
Raw

📊 Superstore Sales Dashboard (Power BI)

📌 Overview

This project is a Power BI dashboard built on the Sample Superstore dataset.
The goal is to identify which products, regions, categories, and customer segments are most profitable and which should be reconsidered.

📄 Dataset

  • Source: Sample Superstore dataset
  • Period: 2014 – 2017
  • Fields: Row ID, Order ID, Order Date, Ship Date, Ship Mode, Customer ID, Customer Name, Segment, Country, City, State, Postal Code, Region, Product ID, Category, Sub-Category, Product Name, Sales, Quantity, Discount, Profit
  • Link: Superstore Dataset on Kaggle

📄 Custom Columns & Measures

Added Columns

  • Profit Margin: [Profit] / [Sales]
  • Order/Ship Date (Year, Month, Date): Split [Order Date] and [Ship Date] into [Year], [Month], [Date]

Key Measures

  • Total Sales
  • Total Profit
  • Profit Margin (%)
  • YoY Sales Growth

🧠 Techniques

  • Data cleaning: Power Query
  • Visualizations: Line chart, Bar chart, Donut chart, Table, Map, KPI Cards
  • Slicers: allow users to filter by Time, Region, and Category
  • Drill-down by Year/Quarter/Month

✅ Key Insights

  • Total Sales: $2.30M across 2014–2017, with +20.34% YoY growth in 2017 vs 2016
  • Sales Seasonality: Sales consistently peak in Q4, highlighting strong seasonal demand
  • West Region: leading region with the highest sales contribution (~32%) and strong profit performance
  • Technology: top-performing category (36% of sales, 17.4% profit margin)
  • Furniture: lowest margin (2.49%) → opportunity for supplier negotiation or product mix optimization

🛠 Tools

  • Power BI Desktop
  • DAX (Data Analysis Expressions)
  • Power Query (data transformation & cleaning)

⭐ This project is part of my learning journey in data analytics with Power BI and demonstrates my ability to transform raw data into actionable business insights.